Unstructured
Turn raw documents into LLM-ready structured data
Unstructured is profiled here as a RAG Framework tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Unstructured converts messy files into clean, structured data for language models, and Brian Raymond founded the company in 2022. The open-source Python library and the commercial platform partition PDFs, Office files, HTML, and email into typed elements, then chunk, enrich, and load the results into vector stores. Data teams use it as the ingestion layer in front of RAG pipelines. The hosted platform adds a no-code workflow UI, scheduled jobs, and page-based billing for teams that outgrow self-managed processing.
Key Capabilities:
Partitioning for 60+ file types into typed document elements
High-resolution layout detection and OCR for scanned documents
Chunking strategies tuned for embedding and retrieval
Source and destination connectors for S3, SharePoint, Pinecone, and other systems
Serverless API plus VPC and on-premises deployment
Apache 2.0 open-source library with Python and JavaScript clients
Alternative tools
- Docling
Open-source document conversion built for RAG pipelines
- Voyage AI
Retrieval-optimized embedding and reranking models
- Chroma
Developer-first embedding database that runs anywhere
- Qdrant
Rust-based vector search engine with rich filtering
- Weaviate
Open-source vector database with native hybrid search
- Pinecone
Managed vector database for production retrieval workloads